Analyzing brain signals of the patients suffering from the state of depression may lead to interesting observations in the signal parameters that is quite different from a normal control. The present study adopts two different methods: Time frequency domain and nonlinear method for the analysis of EEG signals acquired from depression patients and age and sex matched normal controls. The time fr...

1 This research work was funded, in part, by an MRI grant from the National Science Foundation (Grant #: CNS – 0619069). ABSTRACT Code injections can aid successful intrusion attempts, thereby allowing viruses and worms to spread. Current research into intrusion detection is notably focused on application behavior profiling through system call trace analysis. Studying the system call layer has ...

The main purpose of this paper is to study the approximate best proximity pair of cyclic maps and their diameter in fuzzy normed spaces defined by Bag and Samanta. First, approximate best point proximity points on fuzzy normed linear spaces are defined and four general lemmas are given regarding approximate fixed point and approximate best proximity pair of cyclic maps on fuzzy normed spaces. U...

In this study, features of the financial returns of the PSI20index, related to market efficiency, are captured using waveletand entropy-based techniques. This characterization includes the following points. First, the detection of long memory, associated with low frequencies, and a global measure of the time series: the Hurst exponent estimated by several methods, including wavelets. Second, th...

In this paper an approximate decorrelating detector is analyzed on the basis of a rst order ap proximation to the inverse crosscorrelation matrix of signature waveforms The approximation is fairly accurate for systems with low crosscorrelations and is exact in the two user synchronous case We present an exact as well as approximate analysis of the bit error rate performance of this detector on ...

Kernel Canonical correlation analysis (KCCA) is a fundamental method with broad applicability in statistics and machine learning. Although there exist closedform solution to the KCCA objective by solving an N × N eigenvalue system where N is the training set size, the computational requirements of this approach in both memory and time prohibit its usage in the large scale. Various approximation...

During the past few years, several studies have been made on the performance of real-time database systems with respect to the number of transactions that miss their deadlines. These studies have used either simulation models or database testbeds as their performance evaluation tools. We present here a preliminary analytical performance study of real-time transaction processing. Using a series ...

MOTIVATION Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-t...

This paper addresses the problem of generating symbolic test cases for testing the conformance of a black-box implementation with respect to a specification, in the context of reactive systems. The challenge we consider is the selection of test cases according to a test purpose, which is here a set of scenarios of interest that one wants to observe during test execution. Because of the interact...

In this paper we present a method (linear in the size of the formal context) to solve the seriation problem for formal contexts. We show that any maximal solution can be represented by a PQ-Tree. Moreover, the set of PQ-Trees can be seen as a distributive lattice. This lattice yields a consensus method which deals with the multiple solutions.